Getting Set Up

Before we begin, start a new file with File \(\rightarrow\) New File \(\rightarrow\) R Script. As you work through this sheet in the console in R, also add (copy/paste) your commands that work into this new file. At the end, save it, and run to execute all of your commands at once.

Exploring the Data

1.

We will look at GDP per Capita and Life Expectancy using some data from the gapminder project. There is a handy package called `gapminder` that uses a small snippet of this data for exploratory analysis. Install and load the package `gapminder`. Type `?gapminder` and hit enter to see a description of the data.

# first time only
# install.packages("gapminder")

# load gapminder
library(gapminder)

# get help
?gapminder

2

Let’s get a quick look at `gapminder` to see what we’re dealing with.

a.

Get the `str`ucture of the `gapminder` data.

str(gapminder)

## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...

b.

What variables are there?

# - country: a factor
# - continent: a factor
# - year: an integer
# - lifeExp: a number
# - gdpPercap: a number

c.

Look at the `head` of the dataset to get an idea of what the data looks like.

head(gapminder)

d.

Get `summary` statistics of all variables.

summary(gapminder)

##         country        continent        year         lifeExp     
##  Afghanistan:  12   Africa  :624   Min.   :1952   Min.   :23.60  
##  Albania    :  12   Americas:300   1st Qu.:1966   1st Qu.:48.20  
##  Algeria    :  12   Asia    :396   Median :1980   Median :60.71  
##  Angola     :  12   Europe  :360   Mean   :1980   Mean   :59.47  
##  Argentina  :  12   Oceania : 24   3rd Qu.:1993   3rd Qu.:70.85  
##  Australia  :  12                  Max.   :2007   Max.   :82.60  
##  (Other)    :1632                                                
##       pop              gdpPercap       
##  Min.   :6.001e+04   Min.   :   241.2  
##  1st Qu.:2.794e+06   1st Qu.:  1202.1  
##  Median :7.024e+06   Median :  3531.8  
##  Mean   :2.960e+07   Mean   :  7215.3  
##  3rd Qu.:1.959e+07   3rd Qu.:  9325.5  
##  Max.   :1.319e+09   Max.   :113523.1  
##

Simple Plots in Base R

3.

Let’s make sure you can do some basic plots before we get into the `gg`. Use base `R`’s `hist()` function to plot a histogram of `gdpPercap`.

hist(gapminder$gdpPercap)

4.

Use base `R`’s `boxplot()` function to plot a boxplot of `gdpPercap`.

boxplot(gapminder$gdpPercap)

5.

Now make it a boxplot by `continent`.¹

boxplot(gapminder$gdpPercap~gapminder$continent)

# alternate method
# boxplot(gdpPercap~continent, data = gapminder)

6.

Now make a scatterplot of `gdpPercap` on the \(x\)-axis and `LifeExp` on the \(y\)-axis.

plot(gapminder$lifeExp~gapminder$gdpPercap)

# alternate method
# boxplot(lifeExp~gdpPercap, data = gapminder)

Plots with `ggplot2`

7.

Load the package `ggplot2` (you should have installed it previously. If not, install first with `install.packages("ggplot2")`).

# install if you don't have
# install.packages("ggplot2")

# load ggplot2 
library(ggplot2)

8.

Let’s first make a `bar` graph to see how many countries are in each continent. The only `aes`thetic you need is to map `continent` to `x`. Bar graphs are great for representing categories, but not quantitative data.

ggplot(data = gapminder,
       aes(x = continent))+
  geom_bar()

9.

For quantitative data, we want a `histogram` to visualize the distribution of a variable. Make a `histogram` of `gdpPercap`. Your only `aes`thetic here is to map `gdpPercap` to `x`.

ggplot(data = gapminder,
       aes(x = gdpPercap))+
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

10.

Now let’s try adding some color, specifically, add an `aes`thetic that maps `continent` to `fill`.²

ggplot(data = gapminder,
       aes(x = gdpPercap,
           fill = continent))+
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

11.

Instead of a `histogram`, change the `geom` to make it a `density` graph. To avoid overplotting, add `alpha=0.4` to the `geom` argument (alpha changes the transparency of a `fill`).

ggplot(data = gapminder,
       aes(x = gdpPercap,
           fill = continent))+
  geom_density(alpha=0.4)

12.

Redo your plot from 11 for `lifeExp` instead of `gdpPercap`.

ggplot(data = gapminder,
       aes(x = lifeExp,
           fill = continent))+
  geom_density(alpha=0.4)

13.

Now let’s try a scatterplot for `lifeExp` (as `y`) on `gdpPercap` (as `x`). You’ll need both for `aes`thetics. The `geom` here is `geom_point()`.

ggplot(data = gapminder,
       aes(x = gdpPercap,
           y = lifeExp))+
  geom_point()

14.

Add some color by mapping `continent` to `color` in your `aes`thetics.

ggplot(data = gapminder,
       aes(x = gdpPercap,
           y = lifeExp,
           color = continent))+
  geom_point()

15.

Now let’s try adding a regression line with `geom_smooth()`. Add this layer on top of your `geom_point()` layer.

ggplot(data = gapminder,
       aes(x = gdpPercap,
           y = lifeExp,
           color = continent))+
  geom_point()+
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

16.

Did you notice that you got multiple regression lines (colored by continent)? That’s because we set a `global` `aes`thetic of mapping `continent` to `color`. If we want just one regression line, we need to instead move the `color = continent` inside the `aes` of `geom_point`. This will only map `continent` to `color` for points, not for anything else.

ggplot(data = gapminder,
       aes(x = gdpPercap,
           y = lifeExp))+
  geom_point(aes(color = continent))+
  geom_smooth()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

17.

Now add an `aes`thetic to your `point`s to map `pop` to `size`.

ggplot(data = gapminder,
       aes(x = gdpPercap,
           y = lifeExp))+
  geom_point(aes(color = continent, 
                 size = pop))+
  geom_smooth()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

18.

Change the color of the regression line to `"black"`. Try first by putting this inside an `aes()` in your `geom_smooth`, and try a second time by just putting it inside `geom_smooth` without an `aes()`. What’s the difference, and why?

ggplot(data = gapminder,
       aes(x = gdpPercap,
           y = lifeExp))+
  geom_point(aes(color = continent, 
                 size = pop))+
  geom_smooth(aes(color = "black"))

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

# putting it inside aesthetics tries to map color to something
# in the da ta called "black", since R can't find "black", 
# it will produce some random color

ggplot(data = gapminder,
       aes(x = gdpPercap,
           y = lifeExp))+
  geom_point(aes(color = continent, 
                 size = pop))+
  geom_smooth(color = "black")

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

# putting it outside aesthetics (correctly) sets color to black

19.

Another way to separate out continents is with `facet`ing. Add `+facet_wrap(~continent)` to create subplots by `continent`.

ggplot(data = gapminder,
       aes(x = gdpPercap,
           y = lifeExp))+
  geom_point(aes(color = continent, 
                 size = pop))+
  geom_smooth(color = "black")+
  facet_wrap(~continent)

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

20.

Remove the `facet` layer. The `scale` is quite annoying for the `x`-axis, a lot of points are clustered on the lower level. Let’s try changing the scale by adding a layer: `+scale_x_log10()`.

ggplot(data = gapminder,
       aes(x = gdpPercap,
           y = lifeExp))+
  geom_point(aes(color = continent, 
                 size = pop))+
  geom_smooth(color="black")+
  scale_x_log10()

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

21.

Now let’s fix the labels by adding `+labs()`. Inside `labs`, make proper axes titles for `x`, `y`, and a `title` to the plot. If you want to change the name of the legends (continent color), add one for `color` and `size`.

ggplot(data = gapminder,
       aes(x = gdpPercap,
           y = lifeExp))+
  geom_point(aes(color = continent, 
                 size = pop))+
  geom_smooth(color="black")+
  scale_x_log10()+
  labs(x = "GDP per Capita",
       y = "Life Expectancy",
       color = "Continent",
       size = "Population")

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

22.

Now let’s try subsetting by looking only at North America. Take the `gapminder` dataframe and subset it to only look at `continent=="Americas"`). Assign this to a new dataframe object (call it something like `america`.) Now, use this as your `data`, and redo the graph from question 17. (You might want to take a look at your new dataframe to make sure it worked first!)

america<-gapminder[gapminder$continent=="Americas",]

# verify this worked
america

ggplot(data = america,
       aes(x = gdpPercap,
           y = lifeExp))+
  geom_point(aes(color = continent, 
                 size = pop))+
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

23.

Try this again for the whole world, but just for observations in the year 2002.

gap_2002<-gapminder[gapminder$year==2002,]

# verify this worked
gap_2002

ggplot(data = gap_2002,
       aes(x = gdpPercap,
           y = lifeExp))+
  geom_point(aes(color = continent, 
                 size = pop))+
  geom_smooth()

## `geom_smooth()` using method = 'loess' and formula 'y ~ x'

Hint: use formula notation with~.↩︎
In general, color refers to the outside borders of a geom (except points), fill is the interior of an object.↩︎

1.3 — Data Visualization with ggplot2 — R Practice (Answers)

ECON 480 — Fall 2020

Tuesday, August 25, 2020

Getting Set Up

Exploring the Data

1.

2

Let’s get a quick look at gapminder to see what we’re dealing with.

a.

Get the structure of the gapminder data.

b.

What variables are there?

c.

Look at the head of the dataset to get an idea of what the data looks like.

d.

Get summary statistics of all variables.

Simple Plots in Base R

3.

Let’s make sure you can do some basic plots before we get into the gg. Use base R’s hist() function to plot a histogram of gdpPercap.

4.

Use base R’s boxplot() function to plot a boxplot of gdpPercap.

5.

Now make it a boxplot by continent.1

6.

Now make a scatterplot of gdpPercap on the \(x\)-axis and LifeExp on the \(y\)-axis.

Plots with ggplot2

7.

Load the package ggplot2 (you should have installed it previously. If not, install first with install.packages("ggplot2")).

8.

Let’s first make a bar graph to see how many countries are in each continent. The only aesthetic you need is to map continent to x. Bar graphs are great for representing categories, but not quantitative data.

9.

For quantitative data, we want a histogram to visualize the distribution of a variable. Make a histogram of gdpPercap. Your only aesthetic here is to map gdpPercap to x.

10.

Now let’s try adding some color, specifically, add an aesthetic that maps continent to fill.2

11.

Instead of a histogram, change the geom to make it a density graph. To avoid overplotting, add alpha=0.4 to the geom argument (alpha changes the transparency of a fill).

12.

Redo your plot from 11 for lifeExp instead of gdpPercap.

13.

Now let’s try a scatterplot for lifeExp (as y) on gdpPercap (as x). You’ll need both for aesthetics. The geom here is geom_point().

14.

Add some color by mapping continent to color in your aesthetics.

15.

Now let’s try adding a regression line with geom_smooth(). Add this layer on top of your geom_point() layer.

16.

17.

Now add an aesthetic to your points to map pop to size.

18.

Change the color of the regression line to "black". Try first by putting this inside an aes() in your geom_smooth, and try a second time by just putting it inside geom_smooth without an aes(). What’s the difference, and why?

19.

Another way to separate out continents is with faceting. Add +facet_wrap(~continent) to create subplots by continent.

20.

Remove the facet layer. The scale is quite annoying for the x-axis, a lot of points are clustered on the lower level. Let’s try changing the scale by adding a layer: +scale_x_log10().

21.

Now let’s fix the labels by adding +labs(). Inside labs, make proper axes titles for x, y, and a title to the plot. If you want to change the name of the legends (continent color), add one for color and size.

22.

23.